Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
IMAGE: Researchers explore the past, present, and future of smart vehicles and what their integration with smart cities would take. Central to any technological progress is the enrichment of human life. The internet and wireless connectivity have done that by allowing not only virtually anyone anywhere to connect real time, but by making possible connections between humans and a range of intelligent devices both indoors and outdoors, putting smart cities on the horizon. One key aspect of realizing smart cities is "smart vehicles", the latest development in intelligent transportation systems (ITS), which involve the integration of communication, mapping, positioning, network, and sensor technologies to ensure cooperative, efficient, intelligent, safe, and economical transportation. For decades, research on bringing to the streets smart vehicles that operate successfully as part of smart city infrastructure has focused on improving computing paradigms for vehicular network connectivity.
With ambitions to establish a network of autonomous trucking routes across the US, transport startup TuSimple is taking some steady and significant steps forward as it proves its technology through trials and expands into Europe. The latest test run for its self-driving trucks involved hauling a load of fresh produce over hundreds of miles across the US, where it demonstrated that it can complete such tasks in a fast and highly efficient fashion. Previously, we've seen TuSimple's Level 4 autonomous trucks use its variety of cameras and sensors to move goods as part of trials for the US Postal Service and shipping giant UPS. This time around, the startup has partnered with fresh produce provider The Giumarra Companies and Associated Wholesale Grocers to explore autonomous trucking's potential in the fresh food industry. The trial started in Nogales, Arizona, where TuSimple's truck was loaded up with fresh watermelons from Giumarra's facility.
Amid mounting claims its warehouses, especially those with robots, are unsafe, Amazon is doubling down on technology in an attempt to make them safer. The Jeff Bezos-led company is using its Amazon Robotics and Advanced Technology labs to come up with new robots to keep Amazon's warehouse workers, which make up the majority of its more than 1 million employees, safer. Robots known as'Bert' and'Ernie,' use motion-capture technology. Amazon is using technology to keep its warehouses workers safer, despite claims to the contrary. Bert was designed to navigate Amazon's warehouses independently, becoming one of the Jeff Bezos-led company's first autonomous mobile robots This allows Amazon data scientists to understand what's going on in the warehouse and apply that to a laboratory setting, before going back out to the field again.
The world today is thriving on artificial intelligence and the branch technologies associated with it. It is a truth universally acknowledged that the survival of business organizations is heavily contingent on technological advancements induced by AI integration. One such platform is Automation Anywhere that leverages AI and RPA to accelerate and empower business conductions. Automation Anywhere is a reputed global leader in robotic process automation that specializes in offering cloud-native, web-based intelligent automation solutions to empower business operations for companies. Founded in 2003, Automation Anywhere holds a strong legacy of setting benchmarks by AI and RPA adoption.
The automation industry is experiencing an explosion of growth and technology capability. To explain complex technology, we use terms such as "artificial intelligence" to convey the idea that solutions are more capable and advanced than ever before. If you are an investor, business leader, or technology user who seeks to understand the technologies you are investing in, this article is for you. What follows is an explanation of vision-guided robotics and deep-learning algorithms. That's right, the article is titled "artificial intelligence" and yet by the end of the first paragraph, we've already switched to deep-learning algorithms!
"For example, when you type in a search for a job title, say with the phrase'job manager,' the LinkedIn engine will not only look for the title itself, but also people with relevant skills like time management, team coordination, risk assessment and so on," said Sakshi Jain, who is the Engineering Manager on LinkedIn's Responsible AI team. It can index and surface information on people from hundreds of sources as passive candidates typically aren't on job or career sites, and many people tend to only include piecemeal information on their LinkedIn and other profiles." "With the use of automated scheduling and email follow-ups, AI can help free up valuable time and solve the major pain point of extensive back-and-forth coordination with candidates and interviewers." "While there is no need to inspect every transaction or process run by the AI, there is a need to constantly review the performance of the AI steps to ensure that the outputs are in line with expectations. Tom (@ttaulli) is an advisor/board member to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction, The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems and Implementing AI Systems: Transform Your Business in 6 Steps.
According to Stanford Researcher, John McCarthy, "Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial Intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable." Simply put, AI's goal is to make computers/computer programs smart enough to imitate the human mind behaviour. Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings.
Once upon a time, I dated a loving robot. I knew before hand, that it was a one-night stand. I was forewarned that the bot was still in training. But since that first date, I was convinced that "bots" would one day be a viable romantic option. This was over a decade ago when I wasn't so jaded by technology's overwhelming potential. Much later, I met someone loving online.
The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.
There's been a lot of talk around the topic of telesurgery and how far we are from this being a feasible reality. CEO of Asensus Surgical Anthony Fernando says this future is possible through 5G but this infrastructure has to be available everywhere. Moreover, the fundamentals of robotic-assisted surgical practices need to be widespread before we can progress further. How and why tech's big players are poised to give the industry its biggest shakeup in decades. Companies like Asensus have taken steps to digitize the interface between the surgeon and patient through "performance-guided surgery"--the convergence of surgical technology and augmented intelligence.